With the development of the age of Internet consumption, more and more people are choosing to purchase commodities via an e-commerce platform such as an e-commerce websites. An e-commerce website usually recommends a commodity to a user that the user might be interested, through inference based on transaction data, browsing data, and the like in the e-commerce website.
A video content-based data processing technology is required for the e-commerce website such as a video e-commerce system (hereinafter referred to as a target website) to provide users with shopping services that use videos as portals so that commodities associated with objects appearing in various scenarios in the videos are provided to the users.
An existing video content-based recommended object determination method mainly includes: firstly performing an object detection operation and a scenario classification operation on video key frames of a video file viewed by a user to obtain object labels and scenario labels corresponding to the video key frames; querying a commodity library by using the obtained object and scenario labels as query terms; then ranking search results based on correlations; finally merging various ranked results based on a rule to obtain a final association result, and recommending the association result as a recommended object to the user.
The conventional techniques have at least the following problems:
The existing recommended object determination method obtains a query result mainly based on text retrieval, and ambiguity and disorder may often occur in the association result due to many ambiguities included in the text content. Therefore, the existing recommended object determination method may not be able to provide a user with an object precisely associated with video content when recommending an object to the user based on the video content, resulting in poor user experience.